TraceRoot - open-source observability and self-improving layer for AI agents.

About this session

What happens after your AI agent ships — when it silently hallucinates, misfires a tool call, or drifts, and you only hear about it from an angry user? In this talk I'll show how to turn observability into a self-improving loop: Detectors act as an LLM-as-judge over your live traces to catch failures automatically, then an agentic debugger spins up a sandbox with your production source, pinpoints the exact failing line, cross-references your GitHub history, and opens a fix PR. I'll demo this end-to-end on real traces, across frameworks like Claude Agent SDK, and the Vercel AI SDK and coding tools like Claude Code and Codex. You'll leave knowing how to catch silent agent failures before your users do — and how to go from a failing trace to a proposed fix without playing detective.